TGen scientists are working on a variety of research projects funded through the NIH and other sources that develop and examine molecular profiles of human diseases. The focus is to discern complex or simple sets of biomarkers useful for disease diagnosis and prognosis, as well as to develop molecular classification for directing optimal therapeutic choice and identifying new targets. The molecular profile datasets currently being analyzed cover Malignant Gliomas, Melanoma, Pancreatic Cancer, Prostate Cancer, Colon Cancer, Multiple Myeloma, Breast Cancer, Alzheimer's, and Autism. TGen scientists examine molecular profiles from several computational perspectives, using mathematical models with varying degrees of complexity, all of which attempt to identify genes and gene networks that play crucial roles in the molecular pathology. Each of the perspectives involves some aspect of gene-gene interaction, creating a combinatorial problem where computational solutions are limited by the computer's memory size and processing power. TGen has an active group of computational biologists, bioinformaticians, and engineers who are closely working with biomedical and clinical scientists both within and outside TGen to develop various computational and statistical tools that address complex biomedical questions. Examples of the tools developed at TGen include the following: strong feature selection algorithm; network growth algorithm for gene regulatory network inference; Markov Chain-based simulation of gene regulatory networks; selection of cellular context based on microarray and clinical data; clustering of large microarray data; robust error estimation of classification and feature selection algorithms; SNP linkage and coverage analysis; and permutation tests for significance analysis. The data-types that are being analyzed include gene expression arrays, SNPs, CGH, siRNA, and clinical features. Many of these tools require considerable computing power and large amount of memory to examine the enormous complexity of the solution space. Conventional 32-Bit computing architecture cannot address memory above 4 GB. This 4 GB memory limitation imposes suboptimal analytical approaches due to the prohibitively protracted computer analysis time needed for optimal mathematical models and computational algorithms. A 64-Bit computing architecture will allow development of computational models and algorithms that can take a full advantage of memory space beyond 4 GB. The success of TGen scientists to date has come at the sacrifice of time. However, individuals affected with disease do not have the luxury of time. The requested 64-bit SMP computing instrument will optimize TGen researcher's ability to meet their data analysis needs efficiently, fostering timely and effective biomarker discovery for improved human health. [unreadable] [unreadable] [unreadable]